Computational Prediction of Potential Vaccine Candidates From tRNA Encoded peptides (tREP) Using a Bioinformatic...

Area of Science:

• Molecular Biology
• Vaccinology
• Bioinformatics

Background:

• Transfer RNAs (tRNAs) are non-coding RNAs with potential for novel biological applications.
• Epitope-based vaccines offer enhanced safety and specificity compared to traditional vaccines.
• Previous research demonstrated tREP's inhibitory properties against infectious parasites.

Purpose of the Study:

• To investigate tRNA encoded peptides (tREPs) as a novel source for epitope-based vaccines against viral pathogens.
• To develop and validate a computational workflow for identifying potential tREP-derived vaccine candidates.
• To assess the binding affinity and stability of selected tREP candidates using molecular dynamics simulations.

Main Methods:

• Utilized a computational workflow integrating verified data sources and predictive tools.
• Generated a ranked list of plausible epitope-based vaccines from tRNA sequences.
• Performed 200 ns molecular dynamics (MD) simulations and binding free energy calculations for validation.

Main Results:

• Identified two potential tREP-derived epitope-based vaccines: RRHIDIVV for Mamastrovirus 3 and IMVRFSAE for Norovirus GII.
• Computational validation confirmed the binding of these epitopes to predicted HLA molecules.
• MD simulations indicated stability and favorable binding characteristics for the proposed vaccine candidates.

Conclusions:

• tRNA encoded peptides represent an unexplored and promising source for vaccine design.
• The developed computational approach effectively identifies and validates potential tREP-based vaccine candidates.
• RRHIDIVV and IMVRFSAE are computationally validated candidates for Mamastrovirus 3 and Norovirus GII vaccines, respectively.